Enterprises perform quality control to ensure products meet production standards and avoid potential brand reputation damage. As the cost of sensors decreases and connectivity increases, industries adopt real-time imagery analysis to detect quality issues. At the same time, artificial intelligence (AI) advancements enable advanced automation, reduce overall cost and project time, and produce accurate defect detection results in manufacturing plants. As these technologies mature, AI-driven inspections are more common outside of the plant environment. This post describes our SOLVED (Solar Roving Eye Detector) project leveraging machine learning (ML) to identify damaged solar panels using Amazon Rekognition Custom Labels and alert operators to take corrective action.

During Re:Invent 2021 AWS re-launched Amazon Inspector, the software used to discover and manage vulnerabilities on EC2 instances. Originally introduced in 2015, Amazon Inspector simplified the effort of implementing a detection mechanism for both operating systems and applications on EC2 instances and container images residing in Amazon Elastic Container Registry (Amazon ECR). It provides a detailed report including steps for remediation after performing each assessment. This week we were introduced to a new Amazon Inspector replacing what is now called Amazon Inspector Classic. There are significant differences between the two, mostly related to automation, integration with other AWS services, and near real-time performance. The Amazon Inspector is now available in 19 global regions.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. The year 2021 marks a memorable milestone for Amazon Web Services (AWS) as it celebrates both re: Invent's 10th anniversary as well as its 15th anniversary.

This week during its re:Invent 2021 conference in Las Vegas, Amazon announced a slew of new AI and machine learning products and updates across its Amazon Web Services (AWS) portfolio. Touching on DevOps, big data, and analytics, among the highlights were a call summarization feature for Amazon Lex and a capability in CodeGuru that helps detect secrets in source code. Amazon's continued embrace of AI comes as enterprises express a willingness to pilot automation technologies in transitioning their businesses online. Fifty-two percent of companies accelerated their AI adoption plans because of the COVID pandemic, according to a PricewaterhouseCoopers study. Meanwhile, Harris Poll found that 55% of companies accelerated their AI strategy in 2020 and 67% expect to further accelerate their strategy in 2021.

Amazon held its 10th re:Invent conference this week. The annual event, held this year in Las Vegas as well as online, reveals new technologies designed to support its Amazon Web Services (AWS) arm, one of the leading platforms for cloud computing. The tech giant's first AWS re:Invent in 2012 was a humble affair with only 6,000 attendees, dominated by startups and emerging technology partners. The in-person event featured keynote speakers, announcements about its latest tech innovations, as well as training and certification opportunities. Amazon revealed the latest it has to offer, but a few announcements took center stage.

It is the most, exciting and wonderful time of the year. The time AWS gives the customers what they want by launching new tools and services to change the course and destiny of Cloud Computing. AWS is always a pioneer in innovation in the Cloud space. Adam Selipsky was the new CEO of AWS after Andy Jassy was announced as the new CEO and President of Amazon.com I shall be giving a brief summary of some of the announcements made by Adam during his keynote at re:Invent.

Only 9 days until AWS re:Invent 2021, and we're very excited to share some highlights you might enjoy this year. The AI/ML team has been working hard to serve up some amazing content and this year, we have more session types for you to enjoy. Back in person, we now have chalk talks, workshops, builders' sessions, and our traditional breakout sessions. Last year we hosted the first-ever machine learning (ML) keynote, and we are continuing the tradition. We also have more interactive and fun events happening with our AWS DeepRacer League and AWS BugBust Challenge.

The AWS AI/ML Community has compiled a series of session guides to AWS re:Invent 2021 to help you get the most out of re:Invent this year. They covered four distinct categories relevant to AI/ML. With a number of our guide authors attending re:Invent virtually, you will find a balance between virtually accessible sessions and sessions available in-person. The AWS AI/ML Community is a vibrant group of developers, data scientists, researchers, and business decision-makers that dive deep into artificial intelligence and machine learning (ML) concepts, contribute with real-world experiences, and collaborate on building projects together. In this video--which should be required viewing for anyone new to re:Invent--Mike dives deep, beyond simply recommending sessions, with loads of tips and advice for how to make the most of your re:Invent experience--in-person or virtual.

Listen to Dr. Swami Sivasubramanian, Vice President, Amazon Machine earning, and other speakers on the latest key development and innovations in AWS AI & ML. There are new product & service launches, customer stories, demos, and more in this 2-hour Machine Learning keynote session. If you're interested to find out more on the past re: Invent Machine Learning keynote, the full video session and blogs are available below. Hugging Face is a fast-growing, popular, open-source AI/ML community hub for Natural Language Processing (NLP) models, datasets, as well as community ML apps, demo spaces. I am very keen to learn how I can quickly train a Hugging Face transformer NLP model on Amazon SageMaker with just a few lines of code using PyTorch or TensorFlow with SageMaker's distributed training libraries in this workshop.